Sequential design with early stopping (restricted action set) - risk based
Published
May 8, 2025
Modified
May 6, 2025
Load simulation results
# Each input file corresponds to the results from a single simulation# scenario/configuration.# Load all the files into a single list.# files of interestsim_lab <-"sim05-13"# files of interestflist <-list.files(paste0("data/", sim_lab), pattern ="sim05")toks <-list()l <-list()i <-1for(i in1:length(flist)){ l[[i]] <- qs::qread(file.path(paste0("data/", sim_lab), flist[i])) toks[[i]] <-unlist(tstrsplit(flist[i], "[-.]"))}
Results from example trial
The results are from a simulated trial picked from scenario 8 where there is a moderate (OR 1.75) effect of treatment in all domains.
Table 1 shows the decisions made for each domain (or indeterminate if no decisions were made).
Figure 1 shows the knowledge transitions based on the decisions made for each domain by sample size up to the point where the trial was stopped either due to running out of resources or having addressed all the questions.
Initially, all domains start in an indeterminate state in that neither treatment arm is preferred. As the data accrues and analyses progresses, the knowledge state for each domain may transition to, superiority, non-inferiority or futility.
Code
p1 <-ggplot(d_dec_timeline, aes(x = N, y = decision)) +geom_point() +scale_y_discrete("", drop=FALSE) +facet_wrap(domain ~ question, labeller = label_both)suppressWarnings(print(p1))
Of those receiving one-stage (see figure for domain 1) and irrespective of silo membership, approximately 70% are assumed to enter the AB duration domain.
Of those receiving two-stage (see figure for domain 1) and irrespective of silo membership, approximately 90% are assumed to enter the AB ext-proph domain.
Across the entire trial sample, approximately 60% are assumed to enter the AB choice domain.
Here, we know there is an effect of AB choice and would hope that a decision for superiority is made such that new participants are directed to receive rifampacin.